import torch import torch.nn as nn import torch.nn.functional as F from x_transformers import Encoder from models.audio.tts.diffusion_encoder import TimestepEmbeddingAttentionLayers from models.audio.tts.mini_encoder import AudioMiniEncoder from models.audio.tts.unet_diffusion_tts7 import CheckpointedXTransformerEncoder from models.diffusion.nn import timestep_embedding, normalization, zero_module, conv_nd, linear from trainer.networks import register_model def is_latent(t): return t.dtype == torch.float def is_sequence(t): return t.dtype == torch.long class DiffusionTtsFlat(nn.Module): def __init__( self, model_channels=512, num_layers=8, in_channels=100, in_latent_channels=512, in_tokens=8193, max_timesteps=4000, out_channels=200, # mean and variance dropout=0, use_fp16=False, num_heads=16, # Parameters for regularization. layer_drop=.1, unconditioned_percentage=.1, # This implements a mechanism similar to what is used in classifier-free training. ): super().__init__() self.in_channels = in_channels self.model_channels = model_channels self.out_channels = out_channels self.dropout = dropout self.num_heads = num_heads self.unconditioned_percentage = unconditioned_percentage self.enable_fp16 = use_fp16 self.layer_drop = layer_drop self.inp_block = nn.Conv1d(in_channels, model_channels, kernel_size=3, padding=1) time_embed_dim = model_channels self.time_embed = nn.Sequential( linear(model_channels, time_embed_dim), nn.SiLU(), linear(time_embed_dim, time_embed_dim), ) # Either code_converter or latent_converter is used, depending on what type of conditioning data is fed. # This model is meant to be able to be trained on both for efficiency purposes - it is far less computationally # complex to generate tokens, while generating latents will normally mean propagating through a deep autoregressive # transformer network. self.code_converter = nn.Sequential( nn.Embedding(in_tokens, model_channels), CheckpointedXTransformerEncoder( needs_permute=False, max_seq_len=-1, use_pos_emb=False, attn_layers=Encoder( dim=model_channels, depth=3, heads=num_heads, ff_dropout=dropout, attn_dropout=dropout, use_rmsnorm=True, ff_glu=True, rotary_emb_dim=True, ) ) ) self.latent_converter = nn.Conv1d(in_latent_channels, model_channels, 1) # The contextual embedder processes a sample MEL that the output should be "like". self.contextual_embedder = nn.Sequential(nn.Conv1d(in_channels,model_channels,3,padding=1,stride=2), CheckpointedXTransformerEncoder( needs_permute=True, max_seq_len=-1, use_pos_emb=False, attn_layers=Encoder( dim=model_channels, depth=4, heads=num_heads, ff_dropout=dropout, attn_dropout=dropout, use_rmsnorm=True, ff_glu=True, rotary_emb_dim=True, ) )) self.conditioning_conv = nn.Conv1d(model_channels*2, model_channels, 1) self.unconditioned_embedding = nn.Parameter(torch.randn(1,model_channels,1)) # This is a further encoder extension that integrates a timestep signal into the conditioning signal. self.conditioning_timestep_integrator = CheckpointedXTransformerEncoder( needs_permute=True, max_seq_len=-1, use_pos_emb=False, attn_layers=TimestepEmbeddingAttentionLayers( dim=model_channels, timestep_dim=time_embed_dim, depth=3, heads=num_heads, ff_dropout=dropout, attn_dropout=dropout, use_rmsnorm=True, ff_glu=True, rotary_emb_dim=True, layerdrop_percent=0, ) ) self.integrate_conditioning = nn.Conv1d(model_channels*2, model_channels, 1) # This is the main processing module. self.layers = CheckpointedXTransformerEncoder( needs_permute=True, max_seq_len=-1, use_pos_emb=False, attn_layers=TimestepEmbeddingAttentionLayers( dim=model_channels, timestep_dim=time_embed_dim, depth=num_layers, heads=num_heads, ff_dropout=dropout, attn_dropout=dropout, use_rmsnorm=True, ff_glu=True, rotary_emb_dim=True, layerdrop_percent=layer_drop, zero_init_branch_output=True, ) ) self.layers.transformer.norm = nn.Identity() # We don't want the final norm for the main encoder. self.out = nn.Sequential( normalization(model_channels), nn.SiLU(), zero_module(conv_nd(1, model_channels, out_channels, 3, padding=1)), ) def get_grad_norm_parameter_groups(self): groups = { 'minicoder': list(self.contextual_embedder.parameters()), 'conditioning_timestep_integrator': list(self.conditioning_timestep_integrator.parameters()), 'layers': list(self.layers.parameters()), } return groups def get_conditioning_encodings(self, aligned_conditioning, conditioning_input, conditioning_free, return_unused=False): # Shuffle aligned_latent to BxCxS format if is_latent(aligned_conditioning): aligned_conditioning = aligned_conditioning.permute(0, 2, 1) # Note: this block does not need to repeated on inference, since it is not timestep-dependent or x-dependent. unused_params = [] if conditioning_free: code_emb = self.unconditioned_embedding.repeat(conditioning_input.shape[0], 1, 1) else: unused_params.append(self.unconditioned_embedding) cond_emb = self.contextual_embedder(conditioning_input) if len(cond_emb.shape) == 3: # Just take the first element. cond_emb = cond_emb[:, :, 0] if is_latent(aligned_conditioning): code_emb = self.latent_converter(aligned_conditioning) unused_params.extend(list(self.code_converter.parameters())) else: code_emb = self.code_converter(aligned_conditioning) unused_params.extend(list(self.latent_converter.parameters())) cond_emb_spread = cond_emb.unsqueeze(-1).repeat(1, 1, code_emb.shape[-1]) code_emb = self.conditioning_conv(torch.cat([cond_emb_spread, code_emb], dim=1)) # Mask out the conditioning branch for whole batch elements, implementing something similar to classifier-free guidance. if self.training and self.unconditioned_percentage > 0: unconditioned_batches = torch.rand((code_emb.shape[0], 1, 1), device=code_emb.device) < self.unconditioned_percentage code_emb = torch.where(unconditioned_batches, self.unconditioned_embedding.repeat(conditioning_input.shape[0], 1, 1), code_emb) if return_unused: return code_emb, unused_params return code_emb def forward(self, x, timesteps, aligned_conditioning, conditioning_input, conditioning_free=False): """ Apply the model to an input batch. :param x: an [N x C x ...] Tensor of inputs. :param timesteps: a 1-D batch of timesteps. :param aligned_conditioning: an aligned latent or sequence of tokens providing useful data about the sample to be produced. :param conditioning_input: a full-resolution audio clip that is used as a reference to the style you want decoded. :param conditioning_free: When set, all conditioning inputs (including tokens and conditioning_input) will not be considered. :return: an [N x C x ...] Tensor of outputs. """ code_emb, unused_params = self.get_conditioning_encodings(aligned_conditioning, conditioning_input, conditioning_free, return_unused=True) # Everything after this comment is timestep-dependent. time_emb = self.time_embed(timestep_embedding(timesteps, self.model_channels)) code_emb = self.conditioning_timestep_integrator(code_emb, time_emb=time_emb) x = self.inp_block(x) x = self.integrate_conditioning(torch.cat([x, F.interpolate(code_emb, size=x.shape[-1], mode='nearest')], dim=1)) with torch.autocast(x.device.type, enabled=self.enable_fp16): x = self.layers(x, time_emb=time_emb) x = x.float() out = self.out(x) # Involve probabilistic or possibly unused parameters in loss so we don't get DDP errors. extraneous_addition = 0 for p in unused_params: extraneous_addition = extraneous_addition + p.mean() out = out + extraneous_addition * 0 return out @register_model def register_diffusion_tts_flat(opt_net, opt): return DiffusionTtsFlat(**opt_net['kwargs']) if __name__ == '__main__': clip = torch.randn(2, 100, 400) aligned_latent = torch.randn(2,388,512) aligned_sequence = torch.randint(0,8192,(2,388)) cond = torch.randn(2, 100, 400) ts = torch.LongTensor([600, 600]) model = DiffusionTtsFlat(512, layer_drop=.3) # Test with latent aligned conditioning o = model(clip, ts, aligned_latent, cond) # Test with sequence aligned conditioning o = model(clip, ts, aligned_sequence, cond)